Identification of moving objects allows one or more moving objects to be tracked and better allows preventative measures to be taken to avoid a moving object. For example, augmenting a vehicle with a system for identifying moving objects provides a vehicle driver with advance notice of moving objects, simplifying avoidance of the identified moving objects. However, conventional techniques for identifying moving objects are limited in the types of objects that can be identified.
For example, night vision systems use heat and shape to identify moving entities, such as pedestrians, but are unable to identify moving objects that produce minimal heat. Additionally, reliance on object shape limits the type of objects that can be identified by night vision systems. Alternatively, currently used motion segmentation methods use clustering to detect moving objects from a depth ratio histogram. Conventional motion segmentation methods assume a number of clusters for detecting a moving object from the histogram; however, the number of clusters must be initially assumed, leading to inaccuracies in detection. Additionally, the clustering approach used by motion segmentation methods have difficulty detecting small moving objects.